Publication Type : Book Chapter
Publisher : Artificial Intelligence on Medical Data
Source : In Artificial Intelligence on Medical Data, pp. 27-37. Springer, Singapore, 2023.
Url : https://link.springer.com/chapter/10.1007/978-981-19-0151-5_3
Campus : Coimbatore
School : School of Engineering
Department : Center for Computational Engineering and Networking (CEN)
Year : 2022
Abstract : SARS-COV-2, also known as COVID-19 pandemic, has escalated calamity in the entire world. Due to its contagious properties, the disease spreads swiftly from person to person via direct contact. More than 210 million people got infected worldwide with more than 18 million active patients as of August 29, 2021. In numerous places, the test process for COVID-19 detection takes longer than 2 days. Once the patient is affected by COVID-19, the obstruction in lungs causes difficulty in analyzing the presence of other lung diseases, such as variants of pneumonia. In this paper, we propose an enhancement technique via the acclaimed signal processing method called variational mode decomposition (VMD) aiding any X-ray image classification method for the detection of pneumonia using convolutional neural networks (CNN). The experiments were conducted on VGG-16 model loaded with ImageNet weights followed by numerous configurations of dense layers.
Cite this Research Publication : Ramakrishnan, Rohith, Anirudh Vadakedath, Aakash Jignesh Modi, V. V. Sajith Variyar, V. Sowmya, E. A. Gopalakrishnan, and K. P. Soman. "CT Image Enhancement Using Variational Mode Decomposition for AI-Enabled COVID Classification." In Artificial Intelligence on Medical Data, pp. 27-37. Springer, Singapore, 2023.